TESS Background data¶

This notebook shows how to use BackgroundCube to get TESS Full Frame Images (FFI) scatter light component and Earth/Moon angles as pixel maps for every camera/ccd in the instrument.

This data could be used to train a machine learning model or used directly to correct the scatter light contribution in TESS cutouts.

In [1]:

# basic imports
import os
import numpy as np
from tess_backml import BackgroundCube
import matplotlib.pyplot as plt

# increase animation frame limits
import matplotlib

matplotlib.rcParams["animation.embed_limit"] = 2**128

# basic imports import os import numpy as np from tess_backml import BackgroundCube import matplotlib.pyplot as plt # increase animation frame limits import matplotlib matplotlib.rcParams["animation.embed_limit"] = 2**128

In [2]:

# define sector/camera/ccd
sector = 2
camera = 2
ccd = 4

# define sector/camera/ccd sector = 2 camera = 2 ccd = 4

We initialize the object with a specific Sectopr/Camera/CCD, an image bin size, and a method for downsizing (default if 'binning')

In [8]:

bkg_data = BackgroundCube(
    sector=sector, camera=camera, ccd=ccd, img_bin=16, downsize="binning"
)
print(bkg_data)

bkg_data = BackgroundCube( sector=sector, camera=camera, ccd=ccd, img_bin=16, downsize="binning" ) print(bkg_data)

TESS FFI Background object (Sector, Camera, CCD, N times): 2, 2, 4, 1245

Compute the binned scatter light cube which does:

• Finds the darkest frame in the sector to build a star mask and a saturated pixel mask
• Adds a strap mask
• Pulls the FFI flux data from the cloud
• Downsize the image by doing median 16x16 binning, masking out stars, straps, and saturated pixels
• The resulting scatter light cube has shape [nt, 128, 128]

In [7]:

bkg_data.get_scatter_light_cube(frames=None, mask_straps=True, plot=True)

bkg_data.get_scatter_light_cube(frames=None, mask_straps=True, plot=True)

Computing sector darkest frames...
Computing star mask...

Getting FFI flux cube...
Computing average static scene from darkes frames...

Iterating frames: 100%|██████████| 1245/1245 [28:53<00:00,  1.39s/it]

We can animate the scatter light cube for visualization

In [8]:

bkg_data.animate_data(data="sl", save=False, step=4)

bkg_data.animate_data(data="sl", save=False, step=4)

INFO:matplotlib.animation:Animation.save using <class 'matplotlib.animation.HTMLWriter'>

Out[8]:

Once Loop Reflect

Now we compute the Earth/Moon vector maps:

• We pull the Earth/Moon distance, azimuth, and elevation angles from the engineering files. This values a w.r.t. each camera's boresight and changes for each frame in the sector. Using trigonometric rules, we compute these values for each pixel in the downsized grid [nt, 128, 128].
• If ang_size == True then the Earth/Moon distances are changed to angular size.

In [8]:

bkg_data.get_vector_maps(ang_size=True)

bkg_data.get_vector_maps(ang_size=True)

INFO:root:Retrieving TESSVector from: https://heasarc.gsfc.nasa.gov/docs/tess/data/TESSVectors/Vectors/FFI_Cadence/TessVectors_S002_C2_FFI.csv

 96%|█████████▌| 1198/1245 [00:02<00:00, 468.81it/s]/home/jorgemarpa/envs/conda/tess-bkg/lib/python3.10/site-packages/astropy/units/quantity.py:666: RuntimeWarning: divide by zero encountered in divide
  result = super().__array_ufunc__(function, method, *arrays, **kwargs)
/home/jorgemarpa/envs/conda/tess-bkg/lib/python3.10/site-packages/astropy/units/quantity.py:666: RuntimeWarning: invalid value encountered in divide
  result = super().__array_ufunc__(function, method, *arrays, **kwargs)
/home/jorgemarpa/envs/conda/tess-bkg/lib/python3.10/site-packages/astropy/units/quantity.py:666: RuntimeWarning: invalid value encountered in arcsin
  result = super().__array_ufunc__(function, method, *arrays, **kwargs)
100%|██████████| 1245/1245 [00:02<00:00, 495.22it/s]
100%|██████████| 1245/1245 [00:02<00:00, 493.94it/s]
/home/jorgemarpa/projects/tess-backml/src/tess_backml/database/bkg_data.py:487: RuntimeWarning: divide by zero encountered in divide
  self.earth_vectors["dist"] = 2 * np.arctan(const.R_earth.to("km").value / (2 * self.earth_vectors["dist"]))
/home/jorgemarpa/projects/tess-backml/src/tess_backml/database/bkg_data.py:489: RuntimeWarning: divide by zero encountered in divide
  self.moon_vectors["dist"] = 2 * np.arctan(const.R_earth.to("km").value / (2 * self.moon_vectors["dist"]))

We can inspect these maps for a single frame

In [27]:

tdx = 500

fig, ax = plt.subplots(2, 3, figsize=(9,6), sharex=True, sharey=True)

fig.suptitle(
    f"Sector {sector} Camera {camera} CCD {ccd} | "
    f"Frame {tdx} Time {bkg_data.time[tdx]:.3f}"
)
ax[0, 0].imshow(bkg_data.earth_maps["alt"][tdx], origin="lower")
ax[0, 1].imshow(bkg_data.earth_maps["az"][tdx], origin="lower")
ax[0, 2].imshow(bkg_data.earth_maps["dist"][tdx], origin="lower")

ax[0, 0].set_title("Earth Altitude Angle")
ax[0, 1].set_title("Earth Azimuth Angle")
ax[0, 2].set_title("Earth Angular Size")

ax[1, 0].imshow(bkg_data.moon_maps["alt"][tdx], origin="lower")
ax[1, 1].imshow(bkg_data.moon_maps["az"][tdx], origin="lower")
ax[1, 2].imshow(bkg_data.moon_maps["dist"][tdx], origin="lower")

ax[1, 0].set_title("Moon Altitude Angle")
ax[1, 1].set_title("Moon Azimuth Angle")
ax[1, 2].set_title("Moon Angular Size")

ax[0, 0].set_ylabel("Pixel Row")
ax[1, 0].set_ylabel("Pixel Row")
ax[1, 0].set_xlabel("Pixel Column")
ax[1, 1].set_xlabel("Pixel Column")
ax[1, 2].set_xlabel("Pixel Column")

plt.show()

tdx = 500 fig, ax = plt.subplots(2, 3, figsize=(9,6), sharex=True, sharey=True) fig.suptitle( f"Sector {sector} Camera {camera} CCD {ccd} | " f"Frame {tdx} Time {bkg_data.time[tdx]:.3f}" ) ax[0, 0].imshow(bkg_data.earth_maps["alt"][tdx], origin="lower") ax[0, 1].imshow(bkg_data.earth_maps["az"][tdx], origin="lower") ax[0, 2].imshow(bkg_data.earth_maps["dist"][tdx], origin="lower") ax[0, 0].set_title("Earth Altitude Angle") ax[0, 1].set_title("Earth Azimuth Angle") ax[0, 2].set_title("Earth Angular Size") ax[1, 0].imshow(bkg_data.moon_maps["alt"][tdx], origin="lower") ax[1, 1].imshow(bkg_data.moon_maps["az"][tdx], origin="lower") ax[1, 2].imshow(bkg_data.moon_maps["dist"][tdx], origin="lower") ax[1, 0].set_title("Moon Altitude Angle") ax[1, 1].set_title("Moon Azimuth Angle") ax[1, 2].set_title("Moon Angular Size") ax[0, 0].set_ylabel("Pixel Row") ax[1, 0].set_ylabel("Pixel Row") ax[1, 0].set_xlabel("Pixel Column") ax[1, 1].set_xlabel("Pixel Column") ax[1, 2].set_xlabel("Pixel Column") plt.show()

The computed scatter light, angle maps, and times values can be saved to disk as a npz file for future use.

In [20]:

# save data to disk
bkg_data.save_data(out_file=None, save_maps=False)

# save data to disk bkg_data.save_data(out_file=None, save_maps=False)

Saving to ./ffi_cube_bin16_sector002_2-4.npz

In [23]:

data = np.load("./ffi_cube_bin16_sector002_2-4.npz")
data.items()

data = np.load("./ffi_cube_bin16_sector002_2-4.npz") data.items()

Out[23]:

ItemsView(NpzFile './ffi_cube_bin16_sector002_2-4.npz' with keys: scatter_cube, time, cadenceno)

In [24]:

data["scatter_cube"].shape

data["scatter_cube"].shape

Out[24]:

(1245, 128, 128)